This project demonstrates how to classify fruit images using transfer learning with a pre-trained VGG16 model. By leveraging ImageNet knowledge and fine-tuning custom layers, the model learns to classify a smaller fruit dataset efficiently with limited computational resources.
Train a convolutional neural network (CNN) that can classify fruit images into their respective categories.
Transfer Learning lets us reuse pre-trained models for new tasks with smaller datasets.
Data Augmentation improves generalization and helps prevent overfitting.
Fine-tuning specific layers yields better performance than feature extraction alone.
A trained VGG16-based CNN capable of classifying fruit images into multiple categories with high accuracy.